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eISSN: 2572-8466

Applied Biotechnology & Bioengineering

Research Article Volume 9 Issue 5

Immunoinformatic analysis of proteins from DNA replication, repair, recombination, and restriction/modification pathway of Mycobacterium tuberculosis revealed the diagnostic potential of Rv0054 and Rv3644c

Vikas Jha,1 Sathi Maiti,2 Dattatray Sawant,1 Darpan Kaur,1 Sankalp Kasbe,1 Abhishek Kumar,v Badal Saiya,1 Shloka Shukla,1 Simeen Rumani ,1 Mrunmayi Markam1

1National Facility for Biopharmaceuticals, G. N. Khalsa College, India
2Department of Five Years Integrated Course in Bioanalytical Sciences, GNIRD, G.N. Khalsa College, India

Correspondence: Vikas Jha, National Facility for Biopharmaceuticals, G. N. Khalsa College, Matunga, Mumbai, Maharashtra, India, Tel 9892737180

Received: October 15, 2022 | Published: October 27, 2022

Citation: Vikas J, Sathi M, Dattatray S, et al. Immunoinformatic analysis of proteins from DNA replication, repair, recombination, and restriction/modification pathway of Mycobacterium tuberculosis revealed the diagnostic potential of Rv0054 and Rv3644c. J Appl Biotechnol Bioeng. 2022;9(5):190-201. DOI: 10.15406/jabb.2022.09.00309

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Abstract

Mycobacterium tuberculosis being a causative agent of tuberculosis is a powerful pathogen that has evolved to survive within the host. There are certain metabolic pathways that play a vital role in host-pathogen interaction, pathogenicity and virulence which is indicated by the pathophysiology of Mycobacterium tuberculosis (MTB). The pathways involve many proteins that are vital for MTB survival in the host. One such pathway is DNA replication, repair, recombination, and restriction/modification pathway. The study of DNA repair mechanisms in Mycobacterium tuberculosis has progressed more slowly than in other bacteria due to the technological challenges in dealing with a slow-growing pathogen. In this study, by utilizing immunoinformatic analysis & homology modelling approach, the evaluation of the proteins involved in this pathway was carried out which can lead to the discovery of potential drug targets, vaccine candidates as well as various diagnostic markers.

Keywords:In-silco, Mycobacterium tuberculosis, homology modelling, diagnostic markers, vaccine candidates

Abbreviations

ACC, auto cross-covariance; CXC, colorectal cancer; MTB, Mycobacterium tuberculosis; MDR-TB, multidrug-resistant tuberculosis; INH, isoniazid; RFP, rifapim; RNI, Reactive nitrogen intermediates; GRAVY, grand average of hydropathicity index; HMM, hidden markov model; INF, interferon; IP-10, IFN-gamma-inducible protein 10; Th1, type 1 T helper

Introduction

Tuberculosis (TB) is a highly infectious disease caused by Mycobacterium tuberculosis (MTB) that has posed a constant threat throughout human history due to its severe potential implications. The genus Mycobacterium is believed to have originated more than 150 million years ago by Bazin. Mycobacterium tuberculosis is one of the leading infectious agents in most cases of tuberculosis occurring worldwide. MTB having a very ancient origin has survived for over 70,000 years and is currently infecting nearly 5.8 million people around the globe; with new cases of TB each year, nearly one-third of the world's population are carriers of the TB bacillus and are at a chance for creating the active infection at a global scale by WHO. Because of its infectious nature, vigorous immunological response, slow progressive development, and the need for long-term care combating and preventing tuberculosis have been a persisting problem in epidemic history, with the prevalence of multi-drug resistant forms, including its major social repercussions. Multidrug-resistant tuberculosis (MDR-TB), produced by isolates of MTB are resistant to at least two of the most effective anti-TB medications, isoniazid (INH) and rifapim (RFP), hence, still posing complications for TB eradication by Lin and Flynn.

With the continued growth of multi- and extensively resistant MTB strains, undermining the administration of this major catastrophe, new MTB treatments are urgently needed, and metabolic pathways present enticing and potentially powerful targets. There are certain metabolic pathways that play an essential role in host-pathogen interaction, pathogenicity and virulence which is indicated by the pathophysiology of Mycobacterium tuberculosis (MTB). The pathways involve a large number of proteins that are vital for MTB survival in the host.1 Further exploration of various proteins from these pathways, may aid in the development of vaccine candidates, identification of newer drug targets, and even diagnostic markers. One such pathway is DNA replication, repair, recombination and restriction/modification pathway.2 MTB encodes a complex set of proteins that guarantees chromosomal DNA replication and repair. Generally, in bacterial systems, chromosomal replication is carried out by a massive multi-protein replisome that provides high effectiveness and precision in the synthesis of the leading and lagging DNA strands.3 The helicase-primase core complex, and the clamp loader complex are the three catalytic centers that work together to accomplish this. The helicase- primase consists of the DNA-B helicase that uncoils the two DNA strands and the DNA-G primase.4 On the lagging strand, it synthesizes short RNA primers, which the replicative DNA polymerase, Pol IIIa, uses to start replication.5 Most of the constituent proteins in the replisome perform specialty tasks such as DNA unwinding, RNA primer synthesis, clamp loading, and DNA synthesis.6 MTB being an intracellular pathogen is exposed to several highly DNA-damaging attacks in vivo, mainly from antimicrobial reactive oxygen and nitrogen intermediates produced by the host (RNI).7 As a result, having DNA damage repair and reversing pathways that can effectively reverse the damaging impact of these problems is pertinent for bacterial survival. The study of DNA repair mechanisms in MTB has progressed more slowly than in other bacteria due to the technological challenges in dealing with such a slow-growing pathogen. Hence, rather than practical experiments, most conclusions regarding Mycobacterium tuberculosis DNA repair are still focused on insilico methodologies.8 Studying the biochemistry of this pathway can strengthen the knowledge about this disease and can help us consider the DNA replication and repair machinery as a source of new targets for anti-TB drug development.

With over 3924 open reading frames, MTB has the second-largest bacterial genome sequence.9 Moreover, the DNA replication, repair, recombination, and restriction/modification pathway is structurally, but not functionally reported in many studies. In this study, various computational tools have been used to generate biochemical, structural and functional information about all the proteins in this pathway and also to test the applicability of immunoinformatic analysis & homology modelling approach for MTB proteins involved in this pathway.

Methodology

Retrieval of the protein sequence

For the present study 69 amino acid sequences (Table 1) of Mycobacterium tuberculosis involved in DNA replication, repair, recombination, and restriction/modification pathway were retrieved in FASTA format using Mycobrowser database (https://mycobrowser.epfl.ch/).10 Mycobrowser is an exhaustive genomic and proteomic information repository for pathogenic mycobacteria. It provides physically curated annotations and relevant tools to work with genomic and proteomic investigation of these organisms.

Rv ID

Gene name

Description

Score

Rv1317c

alkA

DNA-3-methyladenine glycosidase II

0.4047

Rv2836c

dinF

DNA-damage-inducible protein F

0.503

Rv1329c

dinG

probable ATP-dependent helicase

0.4408

Rv3056

dinP

DNA-damage-inducible protein

0.4442

Rv1537

dinX

probable DNA-damage-inducible protein

0.4677

Rv0001

dnaA

chromosomal replication initiator protein

0.3864

Rv0058

dnaB

DNA helicase (contains intein)

0.4691

Rv1547

dnaE1

DNA polymerase III, α subunit

0.4199

Rv3370c

dnaE2

DNA polymerase III α chain

0.4387

Rv2343c

dnaG

DNA primase

0.5114

Rv0002

dnaN

DNA polymerase III, β subunit

0.6

Rv3711c

dnaQ

DNA polymerase III e chain

0.4202

Rv3721c

dnaZX

DNA polymerase III, γ (dnaZ) and τ (dnaX)

0.5522

Rv2924c

fpg

formamidopyrimidine-DNA glycosylase

0.5759

Rv0006

gyrA

DNA gyrase subunit A

0.5057

Rv0005

gyrB

DNA gyrase subunit B

0.6662

Rv2092c

helY

probable helicase, Ski2 subfamily

0.4458

Rv2101

helZ

probable helicase, Snf2/Rad54 family

0.4457

Rv2756c

hsdM

type I restriction/modification system DNA methylase

0.4363

Rv2755c

hsdS

type I restriction/modification system specificity determinant

0.4672

Rv3296

lhr

ATP-dependent helicase

0.41

Rv3014c

ligA

DNA ligase

0.5559

Rv3062

ligB

DNA ligase

0.5447

Rv3731

ligC

probable DNA ligase

0.5334

Rv1020

mfd

transcription-repair coupling factor

0.4799

Rv2528c

mrr

restriction system protein

0.4972

Rv2985

mutT1

MutT homologue

0.5417

Rv1160

mutT2

MutT homologue

0.5312

Rv0413

mutT3

MutT homologue

0.4964

Rv3589

mutY

probable DNA glycosylase

0.4257

Rv3297

nei

probable endonuclease VIII

0.4816

Rv3674c

nth

probable endonuclease III

0.3086

Rv1316c

ogt

methylated-DNA-protein-cysteine methyltransferase

0.4402

Rv1629

polA

DNA polymerase I

0.5243

Rv1402

priA

putative primosomal protein n' (replication factor Y)

0.4462

Rv3585

radA

probable DNA repair RadA homologue

0.519

Rv2737c

recA

recombinase (contains intein)

0.5066

Rv0630c

recB

exodeoxyribonuclease V

0.5005

Rv0631c

recC

exodeoxyribonuclease V

0.4653

Rv0629c

recD

exodeoxyribonuclease V

0.4881

Rv0003

recF

DNA replication and SOS induction

0.5034

Rv2973c

recG

ATP-dependent DNA helicase

0.5281

Rv1696

recN

recombination and DNA repair

0.516

Rv3715c

recR

RecBC-Independent process of DNA repair

0.4549

Rv2736c

recX

regulatory protein for RecA

0.647

Rv2593c

ruvA

Holliday junction binding protein DNA helicase

0.6785

Rv2592c

ruvB

Holliday junction binding protein

0.4449

Rv2594c

ruvC

Holliday junction resolvase, endodeoxyribonuclease

0.5832

Rv0054

ssb

single strand binding protein

0.7372

Rv1210

tagA

DNA-3-methyladenine glycosidase I

0.4588

Rv3646c

topA

DNA topoisomerase

0.6152

Rv2976c

ung

uracil-DNA glycosylase

0.2138

Rv1638

uvrA

excinuclease ABC subunit A

0.5413

Rv1633

uvrB

excinuclease ABC subunit B

0.4409

Rv1420

uvrC

excinuclease ABC subunit C

0.4965

Rv0949

uvrD

DNA-dependent ATPase I and helicase II

0.3221

Rv3198c

uvrD2

putative UvrD

0.4615

Rv0427c

xthA

exodeoxyribonuclease III

0.5531

Rv0071

-

group II intron maturase

0.4251

Rv0861c

-

probable DNA helicase

0.3907

Rv0944

-

possible formamidopyrimidineDNA glycosylase

0.4262

Rv1688

-

probable 3-methylpurine DNA glycosylase

0.6476

Rv2090

-

partially similar to DNA polymerase I

0.4737

Rv2191

-

similar to both PolC and UvrC proteins

0.4231

Rv2464c

-

probable DNA glycosylase, endonuclease VIII

0.2796

Rv3201c

-

probable ATP-dependent DNA helicase

0.4516

Rv3202c

-

similar to UvrD proteins

0.4949

Rv3263

-

probable DNA methylase

0.4812

Rv3644c

-

similar in N-t

0.7400

Table 1 The following table depicts the genes involved in DNA replication, repair, recombination and restriction/modification pathway and prediction of Antigenicity Score using Vaxijen Server

Prediction of antigenicity of the protein sequences: For the prediction of antigenic properties of the proteins VaxiJen server was used (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html). VaxiJen server is based on auto cross-covariance (ACC) and auto transformation of protein sequences into invariable vectors of major amino acid properties which was used to evaluate the antigenicity of the protein sequence. The VaxiJen server’s algorithm is based on the method of alignment of sequence and to analyse the physicochemical properties of the protein to identify them as antigenic or non-antigenic.

Physicochemical characterization and solubility prediction: The Physicochemical properties of the proteins such as the number of amino acids, pI value, molecular weight, molecular formula, number of atoms, extinction coefficients, estimated half-life, instability index, total number of positively and negatively charged residues, aliphatic index, and grand average of hydropathicity (GRAVY) were analyzed using Expasy’s ProtParam server (https://web.expasy.org/protparam/).11 The total length and solubility of the protein were predicted using the SOSUI server (https://harrier.nagahama-i-bio.ac.jp/sosui/). CYS_REC server (http://www.softberry.com/berry.phtml?topic=cys_rec) was used to analyze the presence of Cysteine residues in the proteins and their bonding patterns.12

Secondary structure prediction: For the prediction of Secondary structures based on the primary sequence of the protein SOPMA (Self-Optimized Prediction Method with Alignment) server (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) was used. With the help of this server, information about the protein such as α-helix, 310 helix, π helix, β bridge, Extended strands, β-turn, bend region and random coil was obtained. When an unknown protein is entered into this server, it searches for all the proteins with similar properties and evolution available in the database.13

Tertiary structure prediction & homology modelling: Homology modelling of the proteins was performed using the Phyre2 tool.14 (http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index) This tool aids in generating automated 3D structures based on comparative methods. The protein sequences were submitted, and the most suitable 3D models were selected on the basis of the highest value of Ramachandran’s favored region. Ramachandran Plot analysis was done using Structural Assessment tool of SWISS-MODEL server (https://swissmodel.expasy.org/interactive). Ramachandran plot analysis provides information on the total number of amino acid residues found in the favourable, allowed, and disallowed regions.15 The accuracy and stereochemical quality of the models were analyzed using ERRAT, Verify3D, PROCHECK, and PROVE from PROCHECK’s server (https://servicesn.mbi.ucla.edu/PROCHECK/). The predicted LG score, MaxSub score and Z-score of the proteins were anticipated using ProQ and ProsA server respectively. ProSA (Protein Structure Analysis) is a popular web tool that is used to verify 3D models of various proteins for possible errors. The output of this tool is an overall quality score in the form of a plot that describes the scores of all experimentally derived protein chains present in the protein data bank.16 ProQ (Protein Quality Predictor) is a tool that is used to predict the quality of a protein model based on neural networks which utilises several structural features such as atom-atom contacts, residue-residue contacts, and solvent-accessible surface area. It differs from other model predictors in a way in which it is optimized to find correct models instead of native structures. MaxScore and LGscore are the two outcome quality measures of this tool where MaxScore has a range between 0 and 1 (insignificant and significant respectively) and LGscore is the negative log P value which indicates structural similarity.

B epitope prediction and scanning protein for IFN epitopes: To anticipate linear B-cell epitopes based on antigen protein sequence features, we used a range of methods, including amino acid scales and HMMs (http://tools.iedb.org/bcell/result/). With a default threshold value of 0.350, the IEDB’s Bepipred method was used to predict linear B-cell epitopes from the conserved region of the proteins.17–19 The Immune Epitope Database (IEDB Emini's) surface accessibility prediction tool was used to predict surface epitopes from the conserved region using the default threshold value of 1.0.20 The antigenic sites were detected using the Kolaskar and Tongaonkar antigenicity approach, with a default threshold value of 1.025.21 BepiPred-2.0 web server was used for predicting B-cell epitopes from antigen sequences.22 Parker Hydrophilicity Prediction23 was used to determine which regions of a protein were on the surface and hence predict the antigenicity of the protein. Chou and Fasman Beta turn prediction was performed for the protein in order to obtain beta turn areas in the query protein, as beta turns play an important role in antigenicity production.24 IFNepitope (https://webs.iiitd.edu.in/raghava/ifnepitope/scan.php) was used for predicting Interferon (INF) gamma inducing regions in the protein. The IFN gamma epitope server predicts IFN epitopes based on a machine learning algorithm called Support Vector Machine by building overlapping sequences. For in silico immunization development, the epitopes with positive outcomes for the IFN-γ reaction were chosen.25

Molecular docking analysis

The idea behind docking is to elicit an effective immune response between an antigen and an antigen receptor. H dock is a web server used for protein protein docking. In this study, both IFN gamma epitopes and B cell epitopes of Rv0054 & Rv3644c were docked with IP-10 protein (Crystal structure of mouse) using H-dock tool (http://hdock.phys.hust.edu.cn/). The workflow of the HDOCK web server is divided into four stages: (1) data input where input of either PDB structure or sequence in FASTA format is accepted (2) sequence similarity search is conducted against the PDB database for the receptor and ligand molecules and for protein input, the HH suite package is used (3) structure modeling is done using MODELLER in which the selected templates undergo sequence alignment using ClustalW. and (4) FFT-based global docking in which priority is given to user-input structures- Based on HDOCKlite, in which an improved shape wise scoring function is used based on putative binding modes by Yan. IP-10 (interferon-gamma-inducible protein) is a chemokine that belongs to the Colorectal cancer (CXC) family and is involved in the pathophysiology of a variety of immunological and inflammatory responses. It also has antifibrotic properties and is a powerful angiostatic factor. IP-10's biological effects are mediated via interactions with the G-protein-coupled receptor CXCR3, which is found on Th1 lymphocytes. IP-10 is thus a potential candidate for anti-inflammatory molecule structure-based rational drug design by Jabeen.

Results and discussion

MTB has long been characterized by a greater mortality rate, and it is predicted to account for 1.4 million tuberculosis fatalities today, second only to the Human Immunodeficiency Virus (HIV) among infectious diseases. Computational studies have provided multiple key molecular-level insights into the repair of damaged DNA by monofunctional DNA glycosylases, the first enzymes to function in the base excision repair pathway that targets non bulky nucleobase modifications.27 Despite of the advancements in medical technologies and extensive research, MTB remains a serious public health concern as currently there are no effective drugs or vaccines established for its treatment. The in-silico technique is the initial step in developing a vaccine, and it is critical that the target protein be chosen correctly. To improve our understanding of host-microbe interactions and obtain a better understanding of their mechanisms, we used in-silico analysis to create a potential new vaccine candidate. Immunoinformatics is a relatively new field that has the potential to speed up immunology research. Computational models are already playing an important role not just in steering the selection of key experiments, but also in the creation of novel testable hypotheses through extensive analysis of complicated immunology data that could not be accomplished using traditional methods alone. The assessment of the various proteins involved in the selected pathway was implemented using this approach which might lead to the discovery of prospective therapeutic targets, vaccine candidates as well as diagnostic indicators.

Prediction of antigenicity of the protein sequences

The sequences were studied for their antigenicity by VaxiJen server keeping the default threshold, and it was found that out of all the sequences, 62 amino acid sequences were antigenic, and they were marked on the basis of the antigenicity score (Table 1). The study revealed several MTB proteins which are potential candidates for the investigation of both cellular and humoral immune responses in an infected host. The antigenicity scores ranged from the highest being 0.7400 to the lowest being 0.2138.

Physicochemical characterization and Solubility prediction

The physicochemical characteristics of all the proteins involved in DNA replication, repair, recombination, and restriction/modification pathway were computed using Expasy’s ProtParam and the resulting values for a number of amino acid residues, theoretical isoelectric point pI, instability index, aliphatic index, and Grand Average Hydropathicity (GRAVY) for each protein were evaluated (Table 2). The theoretical pI values for these proteins range from 4.51 to 10.66. The theoretical isoelectric point can be described as the pH at which a particular molecule carries no net electrical charge, however, protein carries a net negative charge above the isoelectric point and the protein carries a net positive charge, below isoelectrical point. For the pI values, we found that 60% of protein sequences were acidic (pI<7) and 40% of them were basic in nature (pI>7). The acidic nature is helpful as the pathogen is able to tolerate and survive the acidity of phagolysosomes during chronic infection inside the host.28 This data can also be very advantageous for the advancement of the cushion framework for the refinement of protein by an isoelectric centering strategy.13 Further, an instability index of >40 indicates a stable protein and <40 indicates unstable protein.29,30 In our study, 38 sequences (Table 2) had an instability index of less than 40 implying that those protein sequences were stable. The values of the instability index for the proteins range from 20.7 to 93.23. The aliphatic index of a protein is the relative volume occupied by the aliphatic side chains like Alanine (Ala), Valine (Val), Isoleucine (Ile), and Leucine (Leu). The proteins with high aliphatic index values are more thermally stable and in the case of globular proteins, it could be regarded as a positive factor for the increase of thermostability. The Aliphatic index of the protein’s ranges from 64.33 to 133.69 (Table 2), which shows steadiness across a wide range of temperatures. It might very well be viewed as a positive factor for the expansion of the thermostability of globular proteins.31,32 The values of the extinction coefficient specify the amount of light a particular protein can absorb at a certain wavelength and the values of the molar extinction coefficient can be procured if the amino acid composition of the protein is known by Conn. The extinction coefficient values obtained for proteins from ProtParam are of two different types one assuming all pairs of Cys residues form cystines and the other assuming all Cys residues are reduced. The two types of extinction coefficient values for these proteins are almost in the similar range and in some proteins both the values are same; the values range from 4470 to 57885. The grand average hydropathy (GRAVY) value for a protein is calculated as the sum of hydropathy values of all amino acids, divided by the number of residues in the sequence. The soluble nature of the protein helps in DNA packaging in the cell which forms the protein moiety of nucleoprotein. GRAVY values for these proteins lie within -0.474 to 1.023 (Table 2). From all the selected sequences, 63 protein sequences had the grand average hydropathicity (GRAVY) score less than 0 implying that these protein sequences were hydrophilic in nature and soluble in water and could be a good choice for drug designing as it has lower value (Table 2).33,34 This information could clarify whether the protein is globular (hydrophilic) or membranous (hydrophobic) and might give insights into the localization of proteins.

Rv ID

Molecular weight

Theoretical pI

Instability index

Aliphatic index

GRAVY

Rv1317c

53710.49

9.56

36.59

89.23

-0.097

Rv2836c

44737.86

10.41

22.31

133.69

1.023

Rv1329c

70135.47

6.1

34.9

95.5

0.038

Rv3056

37562.17

8.31

43.92

96.18

-0.037

Rv1537

49075.62

5.84

38.21

94.43

-0.062

Rv0001

56548.64

5.45

41.82

86.67

-0.381

Rv0058

96916.74

8.71

42.57

89.11

-0.308

Rv1547

129322.96

5.5

32.68

89.7

-0.199

Rv3370c

116483.67

7.09

39

87.83

-0.122

Rv2343c

69562.95

6.38

40.78

82.28

-0.213

Rv0002

42113.09

4.76

33.1

102.89

0.16

Rv3711c

35749.93

5.71

39.06

101.06

0.035

Rv3721c

61891.31

5.61

44.12

94.12

-0.119

Rv2924c

31950.7

9.98

41.1

90.42

-0.243

Rv0006

92274.31

5.41

38.09

95.68

-0.303

Rv0005

78439.74

6.18

31.31

85.36

-0.379

Rv2092c

99573.95

6.99

46.88

91.13

-0.26

Rv2101

111630.45

5.61

47.79

95.33

-0.2

Rv2756c

60084.11

5.31

41.39

78.44

-0.415

Rv2755c

39211.94

9.61

45.25

95.44

-0.034

Rv3296

161347.7

6.21

41.68

97.51

-0.011

Rv3014c

75257.1

5.42

34.66

91.94

-0.235

Rv3062

53704.57

9.18

30.8

100

0.07

Rv3731

40159.7

6.57

42.6

80.95

-0.337

Rv1020

132908.42

5.55

36.68

96.47

-0.101

Rv2528c

33648.17

5.53

30.75

91.57

-0.305

Rv2985

34748.34

9.28

42.31

82.46

-0.427

Rv1160

15160.28

5.79

21.3

105.25

-0.028

Rv0413

23481.16

5.04

48.69

80.97

-0.353

Rv3589

33684.45

8.85

46.08

86.68

-0.227

Rv3297

28525.6

9.09

33.23

93.69

-0.224

Rv3674c

26998.28

9.83

46.35

96

-0.06

Rv1316c

17858.23

5.92

25.77

91.15

-0.17

Rv1629

98439.98

5.01

33.73

93.23

-0.22

Rv1402

69839.07

9.8

49.68

99.15

-0.006

Rv3585

49881.01

6.74

37.27

100.6

0.132

Rv2737c

85389.06

6.01

28.47

91.89

-0.185

Rv0630c

118722.38

5.98

39.82

93.49

-0.166

Rv0631c

119501.23

6.15

39.71

96.34

-0.17

Rv0629c

61714.72

6.73

32.18

105.58

0.008

Rv0003

42180.2

6.75

40.51

108.7

-0.047

Rv2973c

80328.99

6.16

33.15

97.73

-0.114

Rv1696

62196.88

5.12

27.03

97.39

-0.132

Rv3715c

22119.35

4.99

20.7

105.62

-0.058

Rv2736c

19145.81

9.49

52.22

94.31

-0.399

Rv2593c

20189.23

6.42

26.54

109.59

0.308

Rv2592c

36626.94

5.35

38.65

100.41

0.048

Rv2594c

19753.76

9.22

22.43

99.31

0.122

Rv0054

17321.05

5.12

42.3

64.33

-0.474

Rv1210

22973.04

7.89

53.01

74.22

-0.449

Rv3646c

102317.51

8.23

37.66

82.91

-0.472

Rv2976c

24449.05

9.18

42.03

92.07

0.019

Rv1638

106099.45

6.45

33.9

91.67

-0.247

Rv1633

78038.32

5.05

44.26

93.48

-0.33

Rv1420

71582.11

8.5

39.92

86.53

-0.339

Rv0949

85049.88

5.36

41.51

92.62

-0.273

Rv3198c

75603.69

6.5

35.79

97.41

-0.104

Rv0427c

32108.26

5.15

39.67

80.82

-0.279

Rv0071

26891.83

9.55

40.8

92.04

-0.375

Rv0861c

59772.18

5.73

37.1

98.67

-0.14

Rv0944

16462.95

9.27

29.91

88.99

-0.047

Rv1688

21340.08

9.88

36.44

80.44

-0.231

Rv2090

41938.99

5.58

44.81

89.19

-0.204

Rv2191

69148.12

9.62

49.07

88.79

-0.088

Rv2464c

29681.92

9.88

41.72

82.54

-0.327

Rv3201c

116688.87

5.86

37.89

94.41

-0.02

Rv3202c

110729.42

8.72

44.14

96.31

-0.036

Rv3263

60673.5

8.35

35.3

92.31

-0.103

Rv3644c

41784.51

8.11

42.03

90.02

-0.057

Table 2 Physiochemical characteristics of all the proteins obtained from ExPasy’s ProtParam

Functional analysis of the proteins includes prediction of disulphide bonds and transmembrane region. After distinguishing between membrane and soluble proteins from amino acid sequences, the SOSUI server was used to predict the transmembrane helices for soluble proteins. Though there were cysteine residues present in some of the protein sequences no evidence was found for the presence of disulphide bonds. However, the gene Rv2836c (dinF) shows presence of 12 transmembrane regions among all selected proteins which is an important factor to be considered for the efficacy of drug and, disulphide bridges play an important role in determining thermostability of the protein molecule.35

Secondary structure prediction

On analyzing the proteins using the SOPMA tool (Table 3), the presence of alpha helix is obtained to be dominant in the structures, followed by random coil, extended strand, and beta turns (Mukesh, Prathap, and Sabitha 2013). The default parameters with window width set at 17; similarity threshold set at 8 and division factor set as 4 were considered for the secondary structure prediction.9 In an alpha helix chain, the hydrogen bond forms between the hydrogen atom in the polypeptide backbone amino group of another amino acid that is four amino acids farther along the chain and the oxygen atom in the polypeptide backbone carbonyl group in one amino acid which holds the stretch of amino acids in a right-handed coil. In an alpha helix every helical turn shows presence of 3.6 amino acid residues. The side chains or R groups of the polypeptide protrude out from the α-helix chain which are not involved in the H bonds which help to maintain the alpha helix structure. The models for properties of individual residues and short segments of a polypeptide chain in a random coil contributes a framework for interpreting experimental NMR data for non-native protein conformations.36 Proteins typically have compact, globular shapes assembled by combination of beta sheets. However, they require reversals in the direction of their chain to obtain these compact shapes. The reverse turn is also known as the hairpin bend or beta turn that provides a common structure which satisfies the requirement of chain reversal. Another type of structure responsible for chain reversals which are more complicated than reverse turns are loops. Although they do not show the presence of any periodic structures like beta sheets and alpha helices, they are well defined most of the time and are rigid.37

Gene name

Alpha helix

Extended strand

Beta turn

Random coil

Rv1317c

49.40%

7.26%

5.85%

37.50%

Rv2836c

61.50%

12.30%

5.92%

20.27%

Rv1329c

48.64%

12.95%

4.82%

33.58%

Rv3056

40.46%

17.34%

5.49%

36.71%

Rv1537

39.74%

11.66%

5.18%

43.41%

Rv0001

49.31%

10.65%

3.94%

36.09%

Rv0058

46.57%

13.50%

6.18%

33.75%

Rv1547

47.21%

14.36%

6.76%

31.67%

Rv3370c

45.51%

11.68%

6.86%

35.96%

Rv2343c

51.33%

9.23%

7.67%

31.77%

Rv0002

28.86%

23.88%

5.72%

41.54%

Rv3711c

38.30%

12.46%

7.90%

41.34%

Rv3721c

48.44%

7.96%

5.71%

37.89%

Rv2924c

32.87%

17.99%

6.57%

42.56%

Rv0006

36.87%

21.24%

10.26%

31.62%

Rv0005

41.32%

16.81%

7.00%

34.87%

Rv2092c

52.43%

11.37%

6.95%

29.25%

Rv2101

46.40%

12.73%

4.74%

36.13%

Rv2756c

47.59%

9.26%

4.63%

38.52%

Rv2755c

35.99%

16.76%

5.77%

41.48%

Rv3296

44.42%

12.89%

6.94%

35.76%

Rv3014c

42.40%

13.17%

7.38%

37.05%

Rv3062

52.27%

14.00%

6.90%

26.82%

Rv2592c

50.29%

12.79%

5.52%

31.40%

Rv2594c

52.13%

18.09%

6.91%

22.87%

Rv0054

18.90%

18.29%

7.93%

54.88%

Rv1210

48.04%

3.92%

6.86%

41.18%

Rv3646c

43.25%

12.10%

6.21%

38.44%

Rv2976c

41.41%

15.42%

7.05%

36.12%

Rv1633

52.29%

14.90%

8.60%

24.21%

Rv1420

44.58%

15.48%

4.95%

34.98%

Rv0427c

30.58%

15.81%

9.28%

44.33%

Rv3198c

48.43%

11.43%

34.86%

0.00%

Rv0071

39.15%

14.89%

7.23%

38.72%

Rv0861c

43.17%

18.63%

5.17%

33.03%

Rv0949

48.38%

13.62%

5.97%

32.04%

Rv0944

51.27%

9.49%

3.80%

35.44%

Rv1688

20.20%

25.12%

6.90%

47.78%

Rv2090

33.59%

8.65%

5.34%

52.42%

Rv2191

43.88%

10.54%

3.88%

41.71%

Rv2464c

32.84%

17.91%

5.97%

43.28%

Rv3201c

47.14%

10.72%

3.00%

39.15%

Rv3202c

49.86%

8.06%

4.45%

37.63%

Rv3263

45.75%

16.64%

4.70%

32.91%

Rv3644c

63.34%

9.73%

3.49%

23.44%

Rv1638

36.73%

19.44%

8.54%

35.29%

Rv3731

31.01%

16.48%

6.42%

46.09%

Rv1020

43.03%

13.94%

6.00%

37.03%

Rv2528c

44.44%

12.75%

6.86%

35.95%

Rv2985

31.55%

15.14%

4.73%

48.58%

Rv1160

34.04%

17.02%

6.38%

42.55%

Rv0413

21.66%

21.20%

7.37%

49.77%

Rv3589

50.66%

4.93%

6.25%

38.16%

Rv3297

34.12%

18.04%

5.49%

42.35%

Rv3674c

49.39%

9.39%

6.12%

35.10%

Rv1316c

27.88%

22.42%

8.48%

41.21%

Rv1629

55.53%

9.85%

4.98%

29.65%

Rv1402

35.27%

15.73%

4.73%

44.27%

Rv3585

36.67%

15.42%

8.96%

38.96%

Rv2737c

37.22%

20.25%

8.99%

33.54%

Rv0630c

47.71%

10.69%

4.39%

37.20%

Rv0631c

45.49%

10.03%

3.19%

41.29%

Rv0629c

52.00%

13.22%

5.04%

29.74%

Rv0003

49.09%

16.88%

4.42%

29.61%

Rv2973c

46.68%

13.98%

6.11%

33.24%

Rv1696

58.94%

10.05%

6.13%

24.87%

Rv3715c

40.89%

14.78%

7.39%

36.95%

Rv2736c

71.84%

0.57%

4.02%

23.56%

Rv2593c

47.45%

14.29%

8.67%

29.59%

Table 3 The following table depicts the presence of alpha helix, extended strand, beta turn, and random coil present in the proteins using SOPMA tool

Tertiary structure prediction & homology modelling

Homology modelling of amino acid sequences involved in DNA replication, repair, recombination, and restriction/modification pathway of Mycobacterium tuberculosis was carried out to predict the 3D structure of these sequences. The models for this study were generated using the Phyre2 tool and the models were validated using Ramachandran’s plot analysis. The Ramachandran map is an efficient approach used to visualize the favored regions for backbone dihedral angles ѱ(Psi) against ϕ(Phi) of amino acid residues. The method involves plotting the ϕ(Phi) and the ѱ(Psi) scores on the X-axis and Y-axis respectively with angle spectrum ranging from − 180º to + 180º which predicts the secondary structure and possible conformation of the molecule (Abdullahi et al. 2021). In Ramachandran plot analysis, a good model is expected to have over 90 % of residues in the most favored regions which suggests a good quality of homology models. In this study, it was found that out of all the sequences, 7 protein structures had 100% of residues in the most favored regions and 58 protein structures had 90 % of the residues in the most favored regions (Table 4).

Rv ID

Ramachandran favoured

Ramachandran outliers

Rotamer outliers

Rv1317c

95.70%

3.23

0

Rv2836c

100%

0

0

Rv1329c

89.76%

3.25

0.63

Rv3056

97.97%

0

1.08

Rv1537

95.69%

1.72

1.13

Rv0001

100%

0

0

Rv0058

97.22%

0.46

0.53

Rv1547

93.21%

0.22

7.19

Rv3370c

93.41%

2.69

1.09

Rv2343c

97.37%

0

0

Rv0002

96.67%

1.67

1.01

Rv3711c

91.67%

5.36

0

Rv3721c

93.72%

1.26

3.61

Rv2924c

100%

0

0

Rv0006

98.48%

0

1.04

Rv0005

95.06%

1.23

0

Rv2092c

93.83%

2.3

5.56

Rv2101

93.67%

1.78

1.22

Rv2756c

92.38%

2.42

2.28

Rv2755c

100%

0

0

Rv3296

96.56%

0.73

2.7

Rv3014c

96.55%

0

6.45

Rv3062

94.06%

1.78

2.63

Rv3731

88.65%

5.95%

1.29%

Rv1020

95.05%

0.81%

2.15%

Rv2528c

98.57%

0.00%

0.00%

Rv2985

95.56%

1.27%

2.72%

Rv1160

97.66%

0.00%

0.00%

Rv0413

96.00%

2.40%

0.00%

Rv3589

94.55%

1.98%

4.71%

Rv3297

93.68%

1.19%

1.92%

Rv3674c

95.65%

0.48%

0.00%

Rv1316c

95.29%

2.35%

0.00%

Rv1629

95.92%

0.35%

0.00%

Rv1402

90.17%

6.46%

2.33%

Rv3585

96.62%

0.68%

1.83%

Rv2737c

99.28%

0.00%

0.87%

Rv0630c

92.91%

3.54%

2.55%

Rv0631c

90.41%

3.93%

3.24%

Rv0629c

92.74%

3.07%

0.72%

Rv0003

93.97%

3.45%

0.70%

Rv2973c

91.62%

5.20%

2.15%

Rv1696

96.45%

0.76%

0.34%

Rv3715c

100.00%

0.00%

11.76%

Rv2736c

100.00%

0.00%

4.35%

Rv2593c

95.88%

1.55%

2.80%

Rv2592c

96.78%

1.17%

0.38%

Rv2594c

96.43%

0.00%

0.00%

Rv0054

89.83%

5.08%

4.62%

Rv1210

94.57%

3.26%

0.00%

Rv3646c

93.92%

1.34%

1.34%

Rv2976c

99.11%

0.00%

0.00%

Rv1638

95.49%

0.87%

1.46%

Rv1633

96.07%

1.28%

3.38%

Rv1420

97.67%

0.00%

3.90%

Rv0949

97.50%

0.00%

0.00%

Rv3198c

98.55%

0.00%

0.00%

Rv0427c

93.78%

2.90%

1.00%

Rv0071

94.48%

3.07%

0.71%

Rv0861c

94.07%

0.89%

2.46%

Rv0944

95.00%

1.43%

0.00%

Rv1688

100.00%

0.00%

12.50%

Rv2090

97.77%

0.32%

0.80%

Rv2191

91.25%

4.08%

8.82%

Rv2464c

97.67%

0.00%

0.00%

Rv3201c

90.95%

1.89%

0.83%

Rv3202c

91.59%

1.62%

1.70%

Rv3263

87.48%

3.33%

1.93%

Rv3644c

96.00%

1.09%

0.00%

Table 4 The following table represents the values for Ramachandran plot obtained for the selected proteins

Verify3D analysis indicated that 53 protein structures had a score greater than 0 which conveys that the predicted models were valid. Around 8 protein structures had the ERRAT value more than 95%. ERRAT is a verification algorithm for protein structures that is used for evaluating the quality of crystallographic model building and refinement. Generally, a score above 95% is considered as a good high-resolution structure which indicates that these 8 protein structures were credible and acceptable.38 The ProsA-web server was used to calculate the Z-score of the protein models to determine if the protein model predicted falls within the range of high-quality experimental structures.16,39 The requirement for ProSA-web server is only Cα atoms which helps in the evaluation of approximate models obtained in the structure determination process and low-resolution structures and can be compared against high-resolution structures. The variance of the total energy of the system from an energy distribution resulting from random conformations and shows overall model consistency which is indicated via z-score.40,41 A z-score of -6.07 predicted by ProsA web server (Table 5) represents a good quality model (Prajapat, Bhattachar, and Kumar 2016). Taking that into account, in our study the two genes namely Rv3297 and Rv2593 had a score of -6.18 and -6.19 respectively which concludes that these models are of good quality. ProQ online server was used to forecast the quality of protein sequences used which depends on the neural system constructed apparatus that is based on the evaluation of the structural characters, there is the quality of a protein model, and it is efficient to discover local structures and to revise models. The quality estimates the LG score and MaxSub. The cutoff extent of LG score> 1.5 shows a very incredible model, > 2.5 extraordinary model and > 4 generally extraordinary model and there MaxSub score> 0.1 demonstrates amazingly extraordinary model, > 0.5 extraordinary model and > 0.8 incredibly incredible model. The study showed that all sequences had their LG scores and MaxSub scores as -0.835 and -0.113 (Table 5) respectively which indicates that the standard of all the protein structures is extremely good.42–44 The quality of both global and local structures can be enhanced with this method.

Rv ID

ProQ

ProSA

Predicted LGscore

Predicted MaxSub

Z-Score

Rv1317c

-0.835

-0.113

-4.57

Rv2836c

-0.835

-0.113

-3.94

Rv1329c

-0.835

-0.113

-4.32

Rv3056

-0.835

-0.113

-4.32

Rv1537

-0.835

-0.113

-4.32

Rv0001

-0.835

-0.113

-4.32

Rv0058

-0.835

-0.113

-4.32

Rv1547

-0.835

-0.113

-4.32

Rv3370c

-0.835

-0.113

-4.32

Rv2343c

-0.835

-0.113

-4.32

Rv0002

-0.835

-0.113

-4.32

Rv3711c

-0.835

-0.113

-4.32

Rv3721c

-0.835

-0.113

-4.32

Rv2924c

-0.835

-0.113

-4.32

Rv0006

-0.835

-0.113

-4.32

Rv0005

-0.835

-0.113

-4.32

Rv2092c

-0.835

-0.113

-4.32

Rv2101

-0.835

-0.113

-4.32

Rv2756c

-0.835

-0.113

-4.32

Rv2755c

-0.835

-0.113

-4.32

Rv3296

-0.835

-0.113

-4.32

Rv3014c

-0.835

-0.113

-4.32

Rv3062

-0.835

-0.113

-4.32

Rv3731

-0.835

-0.113

-5.04

Rv1020

-0.835

-0.113

-15.24

Rv2528c

-0.835

-0.113

-5.9

Rv2985

-0.835

-0.113

-8.34

Rv1160

-0.835

-0.113

-5.51

Rv0413

-0.835

-0.113

-3.83

Rv3589

-0.835

-0.113

-6.93

Rv3297

-0.835

-0.113

-6.18

Rv3674c

-0.835

-0.113

-7.47

Rv1316c

-0.835

-0.113

-4.52

Rv1629

-0.835

-0.113

-11.34

Rv1402

-0.835

-0.113

-5.03

Rv3585

-0.835

-0.113

-5.98

Rv2737c

-0.835

-0.113

-5.91

Rv0630c

-0.835

-0.113

-3.02

Rv0631c

-0.835

-0.113

-12.31

Rv0629c

-0.835

-0.113

-5.26

Rv0003

-0.835

-0.113

-5.76

Rv2973c

-0.835

-0.113

-6.6

Rv1696

-0.835

-0.113

-5.5

Rv3715c

-0.835

-0.113

-0.85

Rv2736c

-0.835

-0.113

-2.88

Rv2593c

-0.835

-0.113

-6.19

Rv2592c

-0.835

-0.113

-9.24

Rv2594c

-0.835

-0.113

0.5

Rv0054

-0.835

-0.113

-4.16

Rv1210

-0.835

-0.113

-3.79

Rv3646c

-0.835

-0.113

-12.83

Rv2976c

-0.835

-0.113

-8.57

Rv1638

-0.835

-0.113

-8.67

Rv1633

-0.835

-0.113

-12

Rv1420

-0.835

-0.113

-4.81

Rv0949

-0.835

-0.113

-2.2

Rv3198c

-0.835

-0.113

-6.63

Rv0427c

-0.835

-0.113

-4.98

Rv0071

-0.835

-0.113

-2.32

Rv0861c

-0.835

-0.113

-6.67

Rv0944

-0.835

-0.113

-5.15

Rv1688

-0.835

-0.113

-1.27

Rv2090

-0.835

-0.113

-10.81

Rv2191

-0.835

-0.113

-5

Rv2464c

-0.835

-0.113

-5.55

Rv3201c

-0.835

-0.113

-5.22

Rv3202c

-0.835

-0.113

-9.51

Rv3263

-0.835

-0.113

-5.97

Rv3644c

-0.835

-0.113

-4.57

Table 5 The following table represents the values and overall model quality extracted from webserver ProQ and ProSA

B-cell epitope prediction and scanning of proteins for IFN epitopes

B cell epitope-based prediction was performed for two genes with highest antigenic score Rv0054 & Rv3644c which could be valuable in planning and creating the epitope-based immunization against Mycobacterium tuberculosis. B-cells are an important part of the adaptive immune system because they can protect the body against pathogens and harmful molecules for a long time.22 B-cell epitope assessment is essential for a variety of medical, immunological, and biological applications, including disease control, diagnostics, and vaccine development by Shirai. Intracellular pathogen evasion and recruitment of cytotoxic lymphocytes and natural killer cells are processes in which interferon gamma plays a very significant role.25 The DNA damage pathway includes the recruitment of certain repair enzymes, and the initiation of sign transducers that direct cell cycle and cell survival by Brzostek-Racine. As per the results of B cell prediction for genes with the highest antigen scores: Rv0054 & Rv3644c, IFN gamma inducing regions were predicted and were then proceeded further for molecular docking analysis.

Molecular docking analysis

Following the identification of epitope sequences of Rv0054 & Rv3644c genes, molecular docking was performed with IP-10 protein (Crystal structure of mouse) using H-dock. The docking scores were divided into different ranks of models_rank numbers. RMSD values below 2.0 Å are good docking scores. To this study, only rank 1 model were considered (Table 6). Since the output corresponds to all values below 2.0 Å, it is considered to share a good binding affinity by Ramírez.

Rv3644c

Rv0054

epitopes

Docking score

epitopes

Docking score

ALQCTSGGEPGCGRC

-145.17

AENVAESLTRGARVI

-137.7

CTSGGEPGCGRCRAC

-134.7

ENVAESLTRGARVIV

-128.84

TSGGEPGCGRCRACT

-98

NVAESLTRGARVIVS

-173.54

SGGEPGCGRCRACTT

-152.28

VAESLTRGARVIVSG

-123.27

GGEPGCGRCRACTTT

-157.13

AESLTRGARVIVSGR

-157.84

GEPGCGRCRACTTTL

-157.55

ESLTRGARVIVSGRL

-162.06

GRCRACTTTLAGTHA

-162.32

SLTRGARVIVSGRLK

-136.33

TTLAGTHADVRRVIP

-175.96

LTRGARVIVSGRLKQ

-159.27

VIPEGLSIGVDEMRA

-138.84

TRGARVIVSGRLKQR

-133.56

ANALLKVVEEPPPST

-155.65

RGARVIVSGRLKQRS

-141.54

NALLKVVEEPPPSTV

-157.65

GARVIVSGRLKQRSF

-142.39

ALLKVVEEPPPSTVF

-145.01

RVIVSGRLKQRSFET

-104.77

LLKVVEEPPPSTVFL

-149.37

VIVSGRLKQRSFETR

-137.58

LKVVEEPPPSTVFLL

-156.66

ETREGEKRTVIEVEV

-151.32

KVVEEPPPSTVFLLC

-175.5

EGEKRTVIEVEVDEI

-127.2

EEPPPSTVFLLCAPS

-138.64

VIEVEVDEIGPSLRY

-149.99

EPPPSTVFLLCAPSV

-187.81

VEVDEIGPSLRYATA

-171.98

PPPSTVFLLCAPSVD

-152.19

EVDEIGPSLRYATAK

-182

PSVDPEDIAVTLRSR

-135.32

VDEIGPSLRYATAKV

-162.87

SVDPEDIAVTLRSRC

-165.67

DEIGPSLRYATAKVN

-184.13

VDPEDIAVTLRSRCR

-137.47

EIGPSLRYATAKVNK

-161.33

DPEDIAVTLRSRCRH

-183.87

IGPSLRYATAKVNKA

-161.71

PEDIAVTLRSRCRHV

-170.52

GPSLRYATAKVNKAS

-118.74

EDIAVTLRSRCRHVA

-153.62

PSLRYATAKVNKASR

-143.1

DIAVTLRSRCRHVAL

-149.31

SLRYATAKVNKASRS

-155.24

IAVTLRSRCRHVALV

-180.32

LRYATAKVNKASRSG

-127.98

AVTLRSRCRHVALVT

-153.38

RYATAKVNKASRSGG

-165.53

VTLRSRCRHVALVTP

-170.93

TAKVNKASRSGGFGS

-100.59

TLRSRCRHVALVTPS

-168.09

GSGSRPAPAQTSSAS

-105.05

LRSRCRHVALVTPST

-137.93

SGSRPAPAQTSSASG

-144.57

RSRCRHVALVTPSTH

-113.98

GSRPAPAQTSSASGD

-108.57

SRCRHVALVTPSTHA

-161.97

SRPAPAQTSSASGDD

-120.45

RCRHVALVTPSTHAI

-161.29

SGGFGSGSRPAPAQT

-125.98

CRHVALVTPSTHAIA

-153.05

DDPWGSAPASGSFGG

-120.87

RHVALVTPSTHAIAQ

-134.31

DPWGSAPASGSFGGG

-167.58

LVTPSTHAIAQVLSD

-141.43

PWGSAPASGSFGGGD

-181.42

TANWAASVSGGHVGR

-129.85

WGSAPASGSFGGGDD

-95.74

EELRTALGAGGTGKG

-152.1

ELRTALGAGGTGKGT

-149.44

LRTALGAGGTGKGTG

-148.01

RTALGAGGTGKGTGA

-135.56

TALGAGGTGKGTGAA

-152.1

LGAGGTGKGTGAALR

-112.96

KGTGAALRGATGAMK

-152.21

IDLATYFRDALLVAA

-174.77

AAHAGGVRANHPDMA

-151.49

AHAPPERLLRCIEAV

-180.52

HAPPERLLRCIEAVL

-194.87

APPERLLRCIEAVLA

-158.55

PPERLLRCIEAVLAC

-146.37

EALAVNVKPKFAVDA

-146.73

Table 6 Molecular Docking analysis scores using Hdock

In this study, the selected best proteins have a good immune response to mice protein IP-10. The lead proteins show satisfactory physiochemical properties, antigenicity, secondary and tertiary structures, and molecular docking scores.

Therefore, these proteins can be considered effective against MTB. We believe the findings will benefit in the development of conventional medicine based therapeutic approaches as well as the advancement of better research for future treatment of MTB.45–64

Conclusion

Tuberculosis is a life-threatening disease and a global health challenge. There is an urgent need for potent diagnostic marker against this deadly disease. For this study a total of 69 amino acid sequences involved in the DNA replication, repair, recombination and restriction/modification pathway of Mycobacterium tuberculosis was taken into consideration. Retrieval of the amino acid sequences was done using Tuberculist tool and Mycobrowser. VaxiJen server was used to study the antigenicity of the protein sequences. The physicochemical characterization was done using various computational tools and servers based on different parameters. The distinct parameters were isoelectronic point, molecular weight, instability index, aliphatic index, GRAVY and also the positive & negative residues. SOPMA was used for the analysis of Secondary structure prediction where the alpha helix, 310Helix, Pi helix, Beta bridge, Extended strand, Beta turn, Bend region, random coil, Ambiguous states and other states were predicted. ProtParam was used to study the amino acid composition. Three-dimensional structures were predicted using the Phyre tool. Ramchandran plot maps were analyzed using Swiss model server. ProsA and ProQ servers were used to study the Z-score and LGscore & MaxSub score respectively. This study concludes that the two proteins Rv0054 & Rv3644c can be considered to play a potential role as a diagnostic agent for Mycobacterium Tuberculosis. Computational analysis and homology modelling of Mycobacterium tuberculosis involved in DNA replication, repair, recombination, and restriction/modification pathway provides a basis for analysis of these proteins. This research is believed to set a course for positive outcomes and potential diagnostic markers using immunoinformatic based tools that will aid in the development of remedy against Mycobacterium tuberculosis.

Acknowledgments

None.

Conflicts of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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